Final model XGBOOST 10 MOST IMPORTANT FEAUTURES¶

In [1]:
#Loading the dataset
train <- read.csv('train.preprocessed')
test <- read.csv('test.preprocessed')
options(repr.matrix.max.cols=150, repr.matrix.max.rows=200)
library(pacman)
In [2]:
MYLIBRARIES<-c("mlr",'ggplot2',
               "data.table",'DiagrammeR',
               "xgboost")
pacman::p_load(char=MYLIBRARIES, install=TRUE, character.only = TRUE)
In [3]:
final.model_test <- test
final.model_train <- train
In [4]:
setDF(final.model_train)
In [5]:
final.fact_col <- colnames(final.model_train)[sapply(final.model_train,is.character)] # check for charactar columns and store them in fact_col variable

for(i in final.fact_col) set(final.model_train,j=i,value = factor(final.model_train[[i]]))# convert training data to factor
for (i in final.fact_col) set(final.model_test,j=i,value = factor(final.model_test[[i]]))# convert test data to factor

#create tasks
final.traintask <- makeClassifTask (data = final.model_train,target = "Attrition")  #create classification task for train set
final.testtask <- makeClassifTask (data = final.model_test,target = "Attrition")  #create classification test for train set

#one hot encoding train and test classification tasks
final.traintask <- createDummyFeatures (obj = final.traintask ) 
final.testtask <- createDummyFeatures (obj = final.testtask )
In [6]:
#create learner to be used for model training
lrn <- makeLearner("classif.xgboost",predict.type = "response")
#Specify parameter values for the learner previously defined
lrn$par.vals <- list( objective="binary:logistic", eval_metric="error", nrounds=100L, eta=0.1)

#Set parameters to be used for hyperparameter tuning
params <- makeParamSet(makeIntegerParam("max_depth",lower = 3L,upper = 10L), makeNumericParam("min_child_weight",lower = 1L,upper = 10L), makeNumericParam("subsample",lower = 0.5,upper = 1), makeNumericParam("colsample_bytree",lower = 0.5,upper = 1))

#set resampling strategy to 5 fold cross validation . Set stratify = True
rdesc <- makeResampleDesc("CV",stratify = T,iters=5L)
ctrl <- makeTuneControlRandom(maxit = 10L) #Set number of iterations for random search to a maximum of 10
In [7]:
#train xgboost boost model
set.seed(11)
finalXgboost <- tuneParams(learner = lrn, task = final.traintask, resampling = rdesc, measures = acc, par.set = params, control = ctrl, show.info = T)

[Tune] Started tuning learner classif.xgboost for parameter set:

                    Type len Def   Constr Req Tunable Trafo
max_depth        integer   -   -  3 to 10   -    TRUE     -
min_child_weight numeric   -   -  1 to 10   -    TRUE     -
subsample        numeric   -   - 0.5 to 1   -    TRUE     -
colsample_bytree numeric   -   - 0.5 to 1   -    TRUE     -

With control class: TuneControlRandom

Imputation value: -0

[Tune-x] 1: max_depth=7; min_child_weight=3.85; subsample=0.716; colsample_bytree=0.948


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[2]	train-error:0.124088 
[3]	train-error:0.120438 
[4]	train-error:0.121655 
[5]	train-error:0.121655 
[6]	train-error:0.121655 
[7]	train-error:0.125304 
[8]	train-error:0.121655 
[9]	train-error:0.122871 
[10]	train-error:0.120438 
[11]	train-error:0.115572 
[12]	train-error:0.119221 
[13]	train-error:0.111922 
[14]	train-error:0.114355 
[15]	train-error:0.109489 
[16]	train-error:0.108273 
[17]	train-error:0.107056 
[18]	train-error:0.108273 
[19]	train-error:0.107056 
[20]	train-error:0.105839 
[21]	train-error:0.103406 
[22]	train-error:0.102190 
[23]	train-error:0.100973 
[24]	train-error:0.099757 
[25]	train-error:0.097324 
[26]	train-error:0.097324 
[27]	train-error:0.093674 
[28]	train-error:0.096107 
[29]	train-error:0.086375 
[30]	train-error:0.087591 
[31]	train-error:0.086375 
[32]	train-error:0.086375 
[33]	train-error:0.085158 
[34]	train-error:0.085158 
[35]	train-error:0.081509 
[36]	train-error:0.082725 
[37]	train-error:0.080292 
[38]	train-error:0.081509 
[39]	train-error:0.080292 
[40]	train-error:0.079075 
[41]	train-error:0.075426 
[42]	train-error:0.072993 
[43]	train-error:0.074209 
[44]	train-error:0.074209 
[45]	train-error:0.072993 
[46]	train-error:0.070560 
[47]	train-error:0.071776 
[48]	train-error:0.068127 
[49]	train-error:0.070560 
[50]	train-error:0.069343 
[51]	train-error:0.066910 
[52]	train-error:0.064477 
[53]	train-error:0.062044 
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[56]	train-error:0.060827 
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[68]	train-error:0.048662 
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[71]	train-error:0.046229 
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[100]	train-error:0.035280 
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[3]	train-error:0.135036 
[4]	train-error:0.128954 
[5]	train-error:0.131387 
[6]	train-error:0.126521 
[7]	train-error:0.127737 
[8]	train-error:0.130170 
[9]	train-error:0.127737 
[10]	train-error:0.126521 
[11]	train-error:0.122871 
[12]	train-error:0.122871 
[13]	train-error:0.119221 
[14]	train-error:0.119221 
[15]	train-error:0.114355 
[16]	train-error:0.113139 
[17]	train-error:0.110706 
[18]	train-error:0.111922 
[19]	train-error:0.108273 
[20]	train-error:0.108273 
[21]	train-error:0.108273 
[22]	train-error:0.105839 
[23]	train-error:0.102190 
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[25]	train-error:0.102190 
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[33]	train-error:0.090024 
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[43]	train-error:0.072993 
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[95]	train-error:0.036496 
[96]	train-error:0.035280 
[97]	train-error:0.036496 
[98]	train-error:0.037713 
[99]	train-error:0.037713 
[100]	train-error:0.035280 
[1]	train-error:0.136253 
[2]	train-error:0.116788 
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[4]	train-error:0.124088 
[5]	train-error:0.111922 
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[7]	train-error:0.110706 
[8]	train-error:0.111922 
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[10]	train-error:0.115572 
[11]	train-error:0.108273 
[12]	train-error:0.110706 
[13]	train-error:0.109489 
[14]	train-error:0.103406 
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[16]	train-error:0.102190 
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[18]	train-error:0.096107 
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[21]	train-error:0.092457 
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[94]	train-error:0.029162 
[95]	train-error:0.029162 
[96]	train-error:0.030377 
[97]	train-error:0.030377 
[98]	train-error:0.030377 
[99]	train-error:0.027947 
[100]	train-error:0.029162 

[Tune-y] 1: acc.test.mean=0.8677054; time: 0.0 min

[Tune-x] 2: max_depth=9; min_child_weight=7.12; subsample=0.569; colsample_bytree=0.947


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[1]	train-error:0.164234 
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[3]	train-error:0.148418 
[4]	train-error:0.145985 
[5]	train-error:0.142336 
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[7]	train-error:0.138686 
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[19]	train-error:0.128954 
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[23]	train-error:0.128954 
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[1]	train-error:0.179830 
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[3]	train-error:0.151883 
[4]	train-error:0.153098 
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[Tune-y] 2: acc.test.mean=0.8715937; time: 0.0 min

[Tune-x] 3: max_depth=7; min_child_weight=4.73; subsample=0.807; colsample_bytree=0.93


[1]	train-error:0.136253 
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[4]	train-error:0.125304 
[5]	train-error:0.119221 
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[41]	train-error:0.075426 
[42]	train-error:0.074209 
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[Tune-y] 3: acc.test.mean=0.8667251; time: 0.0 min

[Tune-x] 4: max_depth=6; min_child_weight=7.38; subsample=0.962; colsample_bytree=0.959


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[Tune-y] 4: acc.test.mean=0.8686763; time: 0.0 min

[Tune-x] 5: max_depth=3; min_child_weight=5.75; subsample=0.628; colsample_bytree=0.847


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[Tune-y] 5: acc.test.mean=0.8774378; time: 0.0 min

[Tune-x] 6: max_depth=7; min_child_weight=9.41; subsample=0.592; colsample_bytree=0.822


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[Tune-y] 6: acc.test.mean=0.8764622; time: 0.0 min

[Tune-x] 7: max_depth=10; min_child_weight=6.93; subsample=0.703; colsample_bytree=0.981


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[Tune-y] 7: acc.test.mean=0.8735591; time: 0.0 min

[Tune-x] 8: max_depth=9; min_child_weight=5.52; subsample=0.859; colsample_bytree=0.754


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[Tune-y] 8: acc.test.mean=0.8696519; time: 0.0 min

[Tune-x] 9: max_depth=4; min_child_weight=4.01; subsample=0.793; colsample_bytree=0.764


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[Tune-y] 9: acc.test.mean=0.8648023; time: 0.0 min

[Tune-x] 10: max_depth=9; min_child_weight=8.71; subsample=0.719; colsample_bytree=0.632


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[23]	train-error:0.136087 
[24]	train-error:0.134872 
[25]	train-error:0.136087 
[26]	train-error:0.134872 
[27]	train-error:0.133657 
[28]	train-error:0.134872 
[29]	train-error:0.132442 
[30]	train-error:0.130012 
[31]	train-error:0.128797 
[32]	train-error:0.131227 
[33]	train-error:0.128797 
[34]	train-error:0.130012 
[35]	train-error:0.127582 
[36]	train-error:0.122722 
[37]	train-error:0.125152 
[38]	train-error:0.123937 
[39]	train-error:0.126367 
[40]	train-error:0.122722 
[41]	train-error:0.119077 
[42]	train-error:0.120292 
[43]	train-error:0.119077 
[44]	train-error:0.120292 
[45]	train-error:0.120292 
[46]	train-error:0.119077 
[47]	train-error:0.117861 
[48]	train-error:0.115431 
[49]	train-error:0.117861 
[50]	train-error:0.120292 
[51]	train-error:0.120292 
[52]	train-error:0.116646 
[53]	train-error:0.115431 
[54]	train-error:0.115431 
[55]	train-error:0.115431 
[56]	train-error:0.116646 
[57]	train-error:0.113001 
[58]	train-error:0.113001 
[59]	train-error:0.110571 
[60]	train-error:0.109356 
[61]	train-error:0.109356 
[62]	train-error:0.106926 
[63]	train-error:0.106926 
[64]	train-error:0.109356 
[65]	train-error:0.114216 
[66]	train-error:0.110571 
[67]	train-error:0.109356 
[68]	train-error:0.105711 
[69]	train-error:0.103281 
[70]	train-error:0.105711 
[71]	train-error:0.105711 
[72]	train-error:0.103281 
[73]	train-error:0.103281 
[74]	train-error:0.103281 
[75]	train-error:0.104496 
[76]	train-error:0.102066 
[77]	train-error:0.097205 
[78]	train-error:0.099635 
[79]	train-error:0.100851 
[80]	train-error:0.098420 
[81]	train-error:0.094775 
[82]	train-error:0.095990 
[83]	train-error:0.093560 
[84]	train-error:0.094775 
[85]	train-error:0.092345 
[86]	train-error:0.089915 
[87]	train-error:0.088700 
[88]	train-error:0.092345 
[89]	train-error:0.089915 
[90]	train-error:0.088700 
[91]	train-error:0.087485 
[92]	train-error:0.089915 
[93]	train-error:0.087485 
[94]	train-error:0.087485 
[95]	train-error:0.091130 
[96]	train-error:0.086270 
[97]	train-error:0.085055 
[98]	train-error:0.085055 
[99]	train-error:0.083840 
[100]	train-error:0.083840 

[Tune-y] 10: acc.test.mean=0.8725693; time: 0.0 min

[Tune] Result: max_depth=3; min_child_weight=5.75; subsample=0.628; colsample_bytree=0.847 : acc.test.mean=0.8774378
In [8]:
setDT(final.model_train)  #set train set to data table
setDT(final.model_test)    #set test set to data table
In [9]:
final.train_label <- final.model_train$Attrition  # assign target label for train set to a new field final.train_label
final.test_label <- final.model_test$Attrition     # assign target label for test set to a new field final.test_label
In [10]:
final.new_train <- model.matrix(~.+0,data = final.model_train[,-c("Attrition"),with=F]) # convert final.model_train to matrix format and assign to new variable
final.new_test <- model.matrix(~.+0,data = final.model_test[,-c("Attrition"),with=F])
In [11]:
final.train_label <- as.numeric(final.train_label)-1  #convert labels to numeric - 1 so we have (0,1) instead of(1,2)
final.test_label  <- as.numeric(final.test_label )-1
In [12]:
#Construct xgb.DMatrix object from train set and test set
final.tuned.dtrain <- xgb.DMatrix(data = final.new_train,label = final.train_label )  
final.tuned.dtest <- xgb.DMatrix(data = final.new_test,label = final.test_label )
In [13]:
#assign parameters based on best paramaters from hypertune/cross validation
final.paramsXG <- list(booster = 'gbtree', objective = "binary:logistic", 
               eta=0.3, gamma=0, max_depth= finalXgboost$x$max_depth, 
               min_child_weight=finalXgboost$x$min_child_weight, subsample=finalXgboost$x$subsample,
               colsample_bytree= finalXgboost$x$colsample_bytree)
In [14]:
#Train hypertuned xgb model  using assigned params previously defined 
final.xgb <- xgb.train (params = final.paramsXG, data = final.tuned.dtrain, nrounds = 79, 
                   watchlist = list(val=final.tuned.dtest,train=final.tuned.dtrain), print_every_n = 10, 
                   early_stopping_rounds = 10, maximize = F , eval_metric = "error")

[1]	val-error:0.156109	train-error:0.147860 
Multiple eval metrics are present. Will use train_error for early stopping.
Will train until train_error hasn't improved in 10 rounds.

[11]	val-error:0.149321	train-error:0.115759 
[21]	val-error:0.133484	train-error:0.094358 
[31]	val-error:0.126697	train-error:0.084630 
[41]	val-error:0.131222	train-error:0.077821 
[51]	val-error:0.119910	train-error:0.068093 
[61]	val-error:0.117647	train-error:0.059339 
[71]	val-error:0.117647	train-error:0.053502 
[79]	val-error:0.124434	train-error:0.047665
In [15]:
final.xgbpred <- predict (final.xgb,final.tuned.dtest) #predict model using test set
final.xgbpred <- ifelse(final.xgbpred > 0.5,1,0) #if probablity > 0.5 set prediction to class 1 else set prediction to class 0
final.model_test$Attrition <- as.numeric(final.model_test $Attrition)-1 #convert target label to numeric - 1 so we have (0,1) instead of(1,2)
In [16]:
options(repr.plot.width=8, repr.plot.height=6) 
 
conf_df <- data.frame(table(final.model_test$Attrition, final.xgbpred))

#plotting of confusion matrix using ggplot2 library
ggplot(data =  conf_df, mapping = aes(x = final.xgbpred, y = Var1)) +
  geom_tile(aes(fill = Freq), colour = "white") +
  geom_text(aes(label = sprintf("%1.0f", Freq)), vjust = 1) +
  scale_fill_gradient(low = "#F3F781", high = "#58FA82") + theme(legend.position="none", strip.background = element_blank(), strip.text.x = element_blank(), 
     plot.title=element_text(hjust=0.5, color="white"), plot.subtitle=element_text(colour="white"), plot.background=element_rect(fill="#0D7680"),
                                                        axis.text.x=element_text(colour="white"), axis.text.y=element_text(colour="white"),
                                                        axis.title=element_text(colour="white"), 
     legend.background = element_rect(fill="#FFF9F5",
                                  size=0.5, linetype="solid", 
                                  colour ="black")) + 
labs(title="Confusion Matrix XGBOOST FOR 10 MOST IMPORTANT FEATURES", y="Actual Attrrition", x="Predicted Attrition")
In [17]:
xgb.plot.tree(model=final.xgb, tree=0)
In [18]:
xgb.plot.tree(model=final.xgb, tree=1)
In [ ]: